{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:23:50Z","timestamp":1761582230350,"version":"3.28.0"},"reference-count":20,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,7,17]],"date-time":"2022-07-17T00:00:00Z","timestamp":1658016000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,17]],"date-time":"2022-07-17T00:00:00Z","timestamp":1658016000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,17]]},"DOI":"10.1109\/igarss46834.2022.9884167","type":"proceedings-article","created":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T20:12:24Z","timestamp":1664395944000},"page":"1560-1563","source":"Crossref","is-referenced-by-count":5,"title":["Estimating Uncertainty of Deep Learning Multi-Label Classifications Using Laplace Approximation"],"prefix":"10.1109","author":[{"given":"Ferdinand","family":"Rewicki","sequence":"first","affiliation":[{"name":"German Aerospace Center (DLR), Institute of Data Science Jena,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jakob","family":"Gawlikowski","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), Institute of Data Science Jena,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","first-page":"7025","article-title":"Model and data uncertainty for satellite time series forecasting with deep recurrent models","author":"ru\u00dfwurm","year":"0","journal-title":"IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium"},{"year":"2006","author":"bishop","journal-title":"Pattern Recognition and Machine Learning","key":"ref11"},{"key":"ref12","first-page":"557","article-title":"Practical Gauss-Newton optimisation for deep learning","author":"botev","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning"},{"key":"ref13","first-page":"146: 1","article-title":"New insights and perspectives on the natural gradient method","volume":"21","author":"martens","year":"2020","journal-title":"J Mach Learn Res"},{"key":"ref14","first-page":"12756","article-title":"Stochastic segmentation networks: Modelling spatially correlated aleatoric uncertainty","volume":"33","author":"monteiro","year":"2020","journal-title":"Advances in neural information processing systems"},{"doi-asserted-by":"publisher","key":"ref15","DOI":"10.1023\/A:1009982220290"},{"doi-asserted-by":"publisher","key":"ref16","DOI":"10.3233\/IDA-2011-0499"},{"doi-asserted-by":"publisher","key":"ref17","DOI":"10.1016\/j.entcs.2014.01.025"},{"doi-asserted-by":"publisher","key":"ref18","DOI":"10.1016\/j.isprsjprs.2020.09.020"},{"year":"2021","author":"rewicki","journal-title":"Estimating uncertainty of deep learning multi-label classifications using laplace approxi-mation","key":"ref19"},{"key":"ref4","article-title":"A survey of uncertainty in deep neural networks","author":"gawlikowski","year":"2021","journal-title":"ArXiv Preprint"},{"doi-asserted-by":"publisher","key":"ref3","DOI":"10.14569\/IJACSA.2016.071017"},{"key":"ref6","article-title":"A scalable laplace approximation for neural networks","author":"ritter","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref5","first-page":"1321","article-title":"On calibration of modern neural networks","author":"guo","year":"0","journal-title":"International Conference on Machine Learning"},{"key":"ref8","first-page":"1613","article-title":"Weight uncertainty in neural net-work","author":"blundell","year":"0","journal-title":"International Conference on Machine Learning"},{"key":"ref7","article-title":"Bayesian convolutional neural networks with bernoulli approximate variational inference","author":"gal","year":"2015","journal-title":"ArXiv Preprint"},{"doi-asserted-by":"publisher","key":"ref2","DOI":"10.1109\/CBMS.2019.00072"},{"key":"ref1","first-page":"3780","article-title":"A unified view of multi-label performance measures","author":"wu","year":"0","journal-title":"International Conference on Machine Learning"},{"year":"2019","author":"humt","journal-title":"Laplace approximation for uncertainty estimation of deep neural networks","key":"ref9"},{"key":"ref20","volume":"2","author":"lecun","year":"2010","journal-title":"MNIST Handwritten Digit Database"}],"event":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","start":{"date-parts":[[2022,7,17]]},"location":"Kuala Lumpur, Malaysia","end":{"date-parts":[[2022,7,22]]}},"container-title":["IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9883023\/9883024\/09884167.pdf?arnumber=9884167","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T21:02:08Z","timestamp":1665781328000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9884167\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,17]]},"references-count":20,"URL":"https:\/\/doi.org\/10.1109\/igarss46834.2022.9884167","relation":{},"subject":[],"published":{"date-parts":[[2022,7,17]]}}}